from model import Model
from pySAXS.LS.LSsca import Qlogspace
import numpy
[docs]class SphereGaussAnaDC(Model):
'''
Spheres polydisperses distribution semi-Gaussienne analytique
by DC : 18/06/2009
'''
[docs] def SphereGauss_ana_DC(self,q,par):
"""
q array of q (A-1)
par[0] Mean radius(A)
par[1] Gaussian standard deviation (A)
par[2] concentration of spheres (cm-3)
par[3] scattering length density of spheres (cm-2)
par[4] scattering length density of outside (cm-2)
"""
R = par[0]
s = par[1]
n = par[2]
rho1 = par[3]
rho2 = par[4]
t1 = q*R
t2 = q*s
prefactor = 1e-48*8.*numpy.pi**2.*n*(rho1-rho2)**2./q**6.
fcos = ((1+2.*t2**2.)**2.-t1**2.-t2**2.)*numpy.cos(2.*t1)
fsin = 2.*t1*(1.+2.*t2**2.)*numpy.sin(2.*t1)
f = 1.+t1**2.+t2**2.-numpy.exp(-2.*t2**2)*(fcos+fsin)
return prefactor*f
'''
parameters definition
Model(2,PolyGauss_ana_DC,Qlogspace(1e-4,1.,500.),
([250.,10.,1.5e14,2e11,1e10]),
("Mean (A)",
"Polydispersity ","number density","scattering length density of sphere (cm-2)",
"scattering length density of medium (cm-2)"),
("%f","%f","%1.3e","%1.3e","%1.3e"),
(True,True,False,False,False)),
'''
IntensityFunc=SphereGauss_ana_DC #function
N=0
q=Qlogspace(1e-4,1.,500.) #q range(x scale)
Arg=[250.,10.,1.5e14,2e11,1e10] #list of parameters
Format=["%f","%f","%1.3e","%1.3e","%1.3e"] #list of c format
istofit=[True,True,False,False,False] #list of boolean for fitting
name="Spheres : Semi-Gaussian distribution" #name of the model
Doc=["Mean (A)",\
"Polydispersity ",\
"number density",\
"scattering length density of sphere (cm-2)",\
"scattering length density of medium (cm-2)"] #list of description for parameters
Description="Spheres : Semi-Gaussian distribution, analytical" # description of model
Author="David Carriere" #name of Author
if __name__=="__main__":
'''
test code
'''
modl=SphereGaussAnaDC()
#plot the model
import Gnuplot
gp=Gnuplot.Gnuplot()
gp("set logscale xy")
c=Gnuplot.Data(modl.q,modl.getIntensity(),with_='points')
gp.plot(c)
raw_input("enter")
#plot and fit the noisy model
yn=modl.getNoisy(0.8)
cn=Gnuplot.Data(modl.q,yn,with_='points')
res=modl.fit(yn)
cf=Gnuplot.Data(modl.q,modl.IntensityFunc(modl.q,res),with_='lines')
gp.plot(c,cn,cf)
raw_input("enter")
#plot and fit the noisy model with fitBounds
bounds=modl.getBoundsFromParam() #[250.0,2e11,1e10,1.5e15]
res2=modl.fitBounds(yn,bounds)
print res2
raw_input("enter")